
Although we got decent results with this validation scheme, we were quite far from achieving peak accuracy. Therefore, we sought to improve checkerboard downsampling by improving what we believe is its major flaw, the downsampling itself. Although [1]}, [2]}, [3]} note that there is significant across-scale self-similarity in natural images, the fact remains that downsampling fundamentally distorts the pixel lattice relationship. Therefore, when a neural network trained on full-sized images is applied to downsampled images (or vice-versa), the network will overestimate (or underestimate) the significance of neighboring pixels.
